Probabilistic State of Health Prediction for Lithium-Ion Batteries Based on Incremental Capacity and Differential Voltage Curves

Qingbin Wang, Hangang Yan, Yuxi Wang, Yun Yang, Xiaoguang Liu, Zhuoqi Zhu, Gancai Huang () and Zheng Huang
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Qingbin Wang: Yunfu Power Supply Bureau of Guangdong Power Grid Co., Ltd., Yunfu 527300, China
Hangang Yan: Yunfu Power Supply Bureau of Guangdong Power Grid Co., Ltd., Yunfu 527300, China
Yuxi Wang: Yunfu Power Supply Bureau of Guangdong Power Grid Co., Ltd., Yunfu 527300, China
Yun Yang: Yunfu Power Supply Bureau of Guangdong Power Grid Co., Ltd., Yunfu 527300, China
Xiaoguang Liu: Yunfu Power Supply Bureau of Guangdong Power Grid Co., Ltd., Yunfu 527300, China
Zhuoqi Zhu: Yunfu Power Supply Bureau of Guangdong Power Grid Co., Ltd., Yunfu 527300, China
Gancai Huang: National Institute of Guangdong Advanced Energy Storage Co., Ltd., Guangzhou 510080, China
Zheng Huang: National Institute of Guangdong Advanced Energy Storage Co., Ltd., Guangzhou 510080, China

Energies, 2025, vol. 18, issue 20, 1-16

Abstract: The rapid proliferation of lithium-ion batteries in electric vehicles and grid-scale energy storage systems has underscored the critical need for advanced battery management systems, particularly for accurate state of health (SOH) monitoring. In this study, a hybrid data-driven framework incorporating the whale optimization algorithm (WOA) for Bidirectional Long Short-Term Memory (BiLSTM) networks is introduced. The framework extracts battery aging-related features based on incremental capacity (IC) and differential voltage (DV), which are used as inputs to the SOH prediction model. Then, the BiLSTM network is optimized by WOA to improve convergence performance and model generalization. To further quantify the prediction uncertainty, the Bootstrap approach was used to construct SOH prediction intervals for various confidence levels. Experimental results based on the Oxford dataset show that the proposed WOA-BiLSTM model outperforms the baseline methods including standard LSTM, BiLSTM, and BiGRU. Model performance is evaluated using the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In addition, the integration of Bootstrap enables flexible and reliable interval prediction. The results show that PICP reaches 1 at the 90% confidence level and exceeds 0.85 at the 80% confidence level, with PINAW and CWC metrics validating the interval quality. The proposed method provides accurate point prediction and robust uncertainty quantification, offering a promising tool for smart battery health management.

Keywords: lithium-ion batteries; probabilistic prediction; state of health prediction; whale optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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